MetalDetector 2.0 – Cysteines and Histidines Binding State Predictor

MetalDetector 2.0

:: DESCRIPTION

MetalDetector identifies cysteines and histidines involved in transition metal protein binding sites, starting from the protein sequence alone.

::DEVELOPER

MetalDetector team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
:: DOWNLOAD

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2011 Jul;39(Web Server issue):W288-92. doi: 10.1093/nar/gkr365. Epub 2011 May 16.
MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence.
Passerini A, Lippi M, Frasconi P.

PreDBA 1.1 – Prediction of Protein-DNA Binding Affinity

PreDBA 1.1

:: DESCRIPTION

PreDBA is a computational method that can effectively predict Protein-DNA Binding Affinity using Machine Learning Algorithm.

::DEVELOPER

DLab (Data Mining and Bioinformatics Lab)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

PreDBA

:: MORE INFORMATION

Citation

Yang W, Deng L.
PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity.
Sci Rep. 2020 Jan 28;10(1):1278. doi: 10.1038/s41598-020-57778-1. PMID: 31992738; PMCID: PMC6987227.

EpiDOCK – Molecular Docking – based tool for MHC class II Binding predicition

EpiDOCK

:: DESCRIPTION

EpiDOCK is the first structure-based server for MHC class II binding prediction.

::DEVELOPER

Drug Design Group, Medical University of Sofia

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Protein Eng Des Sel. 2013 Oct;26(10):631-4. doi: 10.1093/protein/gzt018. Epub 2013 May 9.
EpiDOCK: a molecular docking-based tool for MHC class II binding prediction.
Atanasova M1, Patronov A, Dimitrov I, Flower DR, Doytchinova I.

EpiJen 1.0 – Multi-step algorithm for MHC class I binding prediction

EpiJen 1.0

:: DESCRIPTION

EpiJen is a reliable multi-step algorithm for T cell epitope prediction, which belongs to the next generation of in silico T cell epitope identification methods.

::DEVELOPER

Drug Design Group, Medical University of Sofia

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

EpiJen: a server for multistep T cell epitope prediction.
Doytchinova IA, Guan P, Flower DR.
BMC Bioinformatics. 2006 Mar 13;7:131.

EpiTOP 3.0 – Proteochemometrics-based tool for MHC class II Binding Prediction

EpiTOP 1.0

:: DESCRIPTION

EpiTOP is the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins.

::DEVELOPER

Drug Design Group, Medical University of Sofia

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Dimitrov, I., P. Garnev, D. R. Flower, I. Doytchinova
EpiTOP – a proteochemometric tool for MHC class II binding prediction.
Bioinformatics, 26(16), 2066-2068, 2010.

MHCPred 2.0 – Additive method for MHC class I and class II binding prediction.

MHCPred 2.0

:: DESCRIPTION

MHCPred uses the additive method to predict the binding affinity of major histocompatibility complex (MHC) class I and II molecules and also to the Transporter associated with Processing (TAP).

::DEVELOPER

Drug Design Group, Medical University of Sofia

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

MHCPred 2.0: an updated quantitative T-cell epitope prediction server.
Guan P, Hattotuwagama CK, Doytchinova IA, Flower DR.
Appl Bioinformatics. 2006;5(1):55-61.

DNABind – DNA Binding Residue Prediction

DNABind

:: DESCRIPTION

DNABind is a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning- and template-based methods.

::DEVELOPER

Machine Learning and Evolution Laboratory (MLEG)

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web Browser
:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Proteins. 2013 Nov;81(11):1885-99. doi: 10.1002/prot.24330. Epub 2013 Aug 16.
DNABind: a hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches.
Liu R1, Hu J.

HemeBIND / HemeBIND+ – Heme Binding Residue Prediction from Protein Sequence and Structure

HemeBIND / HemeBIND+

:: DESCRIPTION

HemeBIND is an efficient algorithm for predicting heme binding residues by integrating structural and sequence information.

HemeBIND+ predicts heme-binding residues based on the hybrid machine learning (with structural and sequence features) and template-based approaches.

::DEVELOPER

Machine Learning and Evolution Laboratory (MLEG)

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web Browser
:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2011 May 26;12:207. doi: 10.1186/1471-2105-12-207.
HemeBIND: a novel method for heme binding residue prediction by combining structural and sequence information.
Liu R1, Hu J.

CSDeconv 1.03 – Determine Locations of Transcription Factor Binding from ChIP-seq data

CSDeconv 1.03

:: DESCRIPTION

CSDeconv maps transcription factor binding sites from ChIP-seq data to high resolution using a blind deconvolution approach

::DEVELOPER

Desmond Lun

:: SCREENSHOTS

N/a

::REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 CSDeconv

:: MORE INFORMATION

Citation

D. S. Lun, A. Sherrid, B. Weiner, D. R. Sherman, and J. E. Galagan.
A blind deconvolution approach to high-resolution mapping of transcription factor binding sites from ChIP-Seq data.
Genome Biol., 10(12):R142, December 2009.

ABC 1.3 – Allele-specific Binding from ChIP-Seq

ABC 1.3

:: DESCRIPTION

ABC is a computational tool that identifies allele specific binding of transcription factors from aligned ChIP-Seq reads at heterozygous SNVs.

::DEVELOPER

The Computational Biology and Medicine Program (CBMP)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 ABC

:: MORE INFORMATION

Citation

ABC: A tool to identify SNVs causing allele-specific transcription factor binding from ChIP-Seq experiments.
Bailey SD, Virtanen C, Haibe-Kains B, Lupien M.
Bioinformatics. 2015 May 20. pii: btv321.